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Source codes of CIKM2022 Full Paper "Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities"

Home Page: https://dl.acm.org/doi/abs/10.1145/3511808.3557294

Python 100.00%
traffic-forecasting traffic-flow-forecasting spatial-temporal-forecasting spatio-temporal adversarial-learning transfer-learning traffic cikm2022 time-series time-series-forecasting

dastnet's Introduction

DASTNet

CIKM 2022: Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities

PWC

PWC

PWC

PWC

PWC

PWC

PWC

PWC

PWC

Requirements

  • python == 3.6
  • torch == 1.7.0+cu110
  • networkx == 2.4
  • gensim == 3.8.1

See requirements.txt for more details.

Dataset

  • PeMS04
  • PeMS07
  • PeMS08

Please refer to STSGCN (AAAI2020).

Model

Run

python main.py

Reference

Please cite our paper if you use the model in your own work:

@inproceedings{tang2022domain,
  title={Domain adversarial spatial-temporal network: A transferable framework for short-term traffic forecasting across cities},
  author={Tang, Yihong and Qu, Ao and Chow, Andy HF and Lam, William HK and Wong, SC and Ma, Wei},
  booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
  pages={1905--1915},
  year={2022}
}

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